Overview

Dataset statistics

Number of variables18
Number of observations36008
Missing cells94400
Missing cells (%)14.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 MiB
Average record size in memory144.0 B

Variable types

Numeric11
Text3
Unsupported2
Categorical1
DateTime1

Alerts

latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
number_of_reviews is highly overall correlated with number_of_reviews_ltm and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
reviews_per_month is highly overall correlated with number_of_reviews and 1 other fieldsHigh correlation
room_type is highly imbalanced (58.4%)Imbalance
neighbourhood_group has 36008 (100.0%) missing valuesMissing
price has 1771 (4.9%) missing valuesMissing
last_review has 10302 (28.6%) missing valuesMissing
reviews_per_month has 10302 (28.6%) missing valuesMissing
license has 36008 (100.0%) missing valuesMissing
price is highly skewed (γ1 = 66.96707573)Skewed
minimum_nights is highly skewed (γ1 = 28.22092657)Skewed
id has unique valuesUnique
neighbourhood_group is an unsupported type, check if it needs cleaning or further analysisUnsupported
license is an unsupported type, check if it needs cleaning or further analysisUnsupported
number_of_reviews has 10302 (28.6%) zerosZeros
availability_365 has 5475 (15.2%) zerosZeros
number_of_reviews_ltm has 14214 (39.5%) zerosZeros

Reproduction

Analysis started2024-04-25 21:05:27.420478
Analysis finished2024-04-25 21:07:23.434123
Duration1 minute and 56.01 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct36008
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4457301 × 1017
Minimum17878
Maximum1.0538233 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:24.918353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum17878
5-th percentile2958870.7
Q128637937
median5.7613679 × 1017
Q38.7267609 × 1017
95-th percentile1.0398874 × 1018
Maximum1.0538233 × 1018
Range1.0538233 × 1018
Interquartile range (IQR)8.7267609 × 1017

Descriptive statistics

Standard deviation4.4185817 × 1017
Coefficient of variation (CV)0.99389336
Kurtosis-1.8169913
Mean4.4457301 × 1017
Median Absolute Deviation (MAD)4.7330968 × 1017
Skewness0.089169612
Sum-3.5887837 × 1018
Variance1.9523864 × 1035
MonotonicityNot monotonic
2024-04-25T18:07:26.011813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17878 1
 
< 0.1%
7.963583977 × 10171
 
< 0.1%
7.971095024 × 10171
 
< 0.1%
7.971143587 × 10171
 
< 0.1%
7.971144518 × 10171
 
< 0.1%
7.971145873 × 10171
 
< 0.1%
7.935195318 × 10171
 
< 0.1%
7.935705612 × 10171
 
< 0.1%
7.963588726 × 10171
 
< 0.1%
7.970988872 × 10171
 
< 0.1%
Other values (35998) 35998
> 99.9%
ValueCountFrequency (%)
17878 1
< 0.1%
25026 1
< 0.1%
35764 1
< 0.1%
41198 1
< 0.1%
48305 1
< 0.1%
48901 1
< 0.1%
49179 1
< 0.1%
50759 1
< 0.1%
51703 1
< 0.1%
53533 1
< 0.1%
ValueCountFrequency (%)
1.053823262 × 10181
< 0.1%
1.053808194 × 10181
< 0.1%
1.05378934 × 10181
< 0.1%
1.053756202 × 10181
< 0.1%
1.053746406 × 10181
< 0.1%
1.053743193 × 10181
< 0.1%
1.053736264 × 10181
< 0.1%
1.053720091 × 10181
< 0.1%
1.053716797 × 10181
< 0.1%
1.053711356 × 10181
< 0.1%

name
Text

Distinct14150
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:27.026137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length168
Median length93
Mean length63.446845
Min length31

Characters and Unicode

Total characters2284594
Distinct characters93
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10847 ?
Unique (%)30.1%

Sample

1st rowCondo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bath
2nd rowRental unit in Rio de Janeiro · 1 bedroom · 1 bed · 1 bath
3rd rowCondo in Rio de Janeiro · ★4.57 · 1 bedroom · 1 bed · 1 bath
4th rowRental unit in Rio de Janeiro · ★4.72 · 1 bedroom · 1 bed · 1 bath
5th rowRental unit in Rio de Janeiro · ★4.81 · 2 bedrooms · 3 beds · 2 baths
ValueCountFrequency (%)
· 132069
23.3%
1 52893
 
9.4%
in 36008
 
6.4%
2 27550
 
4.9%
unit 26976
 
4.8%
rental 26976
 
4.8%
rio 25052
 
4.4%
de 24336
 
4.3%
janeiro 24223
 
4.3%
beds 23303
 
4.1%
Other values (494) 166266
29.4%
2024-04-25T18:07:29.659971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
529873
23.2%
e 170589
 
7.5%
o 138339
 
6.1%
· 132069
 
5.8%
n 126164
 
5.5%
a 125079
 
5.5%
i 120678
 
5.3%
b 110655
 
4.8%
d 105714
 
4.6%
t 100258
 
4.4%
Other values (83) 625176
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2284594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
529873
23.2%
e 170589
 
7.5%
o 138339
 
6.1%
· 132069
 
5.8%
n 126164
 
5.5%
a 125079
 
5.5%
i 120678
 
5.3%
b 110655
 
4.8%
d 105714
 
4.6%
t 100258
 
4.4%
Other values (83) 625176
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2284594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
529873
23.2%
e 170589
 
7.5%
o 138339
 
6.1%
· 132069
 
5.8%
n 126164
 
5.5%
a 125079
 
5.5%
i 120678
 
5.3%
b 110655
 
4.8%
d 105714
 
4.6%
t 100258
 
4.4%
Other values (83) 625176
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2284594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
529873
23.2%
e 170589
 
7.5%
o 138339
 
6.1%
· 132069
 
5.8%
n 126164
 
5.5%
a 125079
 
5.5%
i 120678
 
5.3%
b 110655
 
4.8%
d 105714
 
4.6%
t 100258
 
4.4%
Other values (83) 625176
27.4%

host_id
Real number (ℝ)

Distinct21980
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.784179 × 108
Minimum1671
Maximum5.5271448 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:30.641631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1671
5-th percentile2981903.8
Q124167859
median96445730
Q33.2560925 × 108
95-th percentile5.0692743 × 108
Maximum5.5271448 × 108
Range5.5271281 × 108
Interquartile range (IQR)3.0144139 × 108

Descriptive statistics

Standard deviation1.7696843 × 108
Coefficient of variation (CV)0.99187597
Kurtosis-0.90013792
Mean1.784179 × 108
Median Absolute Deviation (MAD)86663858
Skewness0.75158178
Sum6.4244719 × 1012
Variance3.1317825 × 1016
MonotonicityNot monotonic
2024-04-25T18:07:31.736152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000862 185
 
0.5%
91654021 157
 
0.4%
341887136 145
 
0.4%
1982737 142
 
0.4%
47584281 142
 
0.4%
371026651 128
 
0.4%
14315601 85
 
0.2%
74463624 80
 
0.2%
30165706 77
 
0.2%
12909867 69
 
0.2%
Other values (21970) 34798
96.6%
ValueCountFrequency (%)
1671 1
 
< 0.1%
3607 1
 
< 0.1%
11739 9
< 0.1%
19065 3
 
< 0.1%
34105 2
 
< 0.1%
37072 1
 
< 0.1%
48024 7
< 0.1%
60098 1
 
< 0.1%
64036 2
 
< 0.1%
68997 1
 
< 0.1%
ValueCountFrequency (%)
552714482 1
< 0.1%
552420950 1
< 0.1%
552339316 1
< 0.1%
552326533 1
< 0.1%
552283430 1
< 0.1%
552282629 1
< 0.1%
552267363 1
< 0.1%
552264669 1
< 0.1%
552258899 1
< 0.1%
552258099 1
< 0.1%
Distinct6268
Distinct (%)17.4%
Missing9
Missing (%)< 0.1%
Memory size281.4 KiB
2024-04-25T18:07:33.290154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length32
Mean length7.3657602
Min length1

Characters and Unicode

Total characters265160
Distinct characters105
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3526 ?
Unique (%)9.8%

Sample

1st rowMatthias
2nd rowZeilma , Da
3rd rowBob
4th rowViviane
5th rowMaria José
ValueCountFrequency (%)
maria 894
 
2.1%
ana 581
 
1.3%
rio 516
 
1.2%
carlos 493
 
1.1%
luiz 382
 
0.9%
daniel 359
 
0.8%
rodrigo 336
 
0.8%
ricardo 332
 
0.8%
pedro 331
 
0.8%
paulo 319
 
0.7%
Other values (5070) 38787
89.5%
2024-04-25T18:07:35.457345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 40312
15.2%
i 24676
 
9.3%
e 21035
 
7.9%
r 18618
 
7.0%
o 18004
 
6.8%
n 17664
 
6.7%
l 14927
 
5.6%
s 9166
 
3.5%
u 7752
 
2.9%
d 7368
 
2.8%
Other values (95) 85638
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 265160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 40312
15.2%
i 24676
 
9.3%
e 21035
 
7.9%
r 18618
 
7.0%
o 18004
 
6.8%
n 17664
 
6.7%
l 14927
 
5.6%
s 9166
 
3.5%
u 7752
 
2.9%
d 7368
 
2.8%
Other values (95) 85638
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 265160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 40312
15.2%
i 24676
 
9.3%
e 21035
 
7.9%
r 18618
 
7.0%
o 18004
 
6.8%
n 17664
 
6.7%
l 14927
 
5.6%
s 9166
 
3.5%
u 7752
 
2.9%
d 7368
 
2.8%
Other values (95) 85638
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 265160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 40312
15.2%
i 24676
 
9.3%
e 21035
 
7.9%
r 18618
 
7.0%
o 18004
 
6.8%
n 17664
 
6.7%
l 14927
 
5.6%
s 9166
 
3.5%
u 7752
 
2.9%
d 7368
 
2.8%
Other values (95) 85638
32.3%

neighbourhood_group
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36008
Missing (%)100.0%
Memory size281.4 KiB
Distinct156
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:37.130100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length24
Median length22
Mean length10.281743
Min length3

Characters and Unicode

Total characters370225
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowCopacabana
2nd rowFlamengo
3rd rowCopacabana
4th rowCopacabana
5th rowCopacabana
ValueCountFrequency (%)
copacabana 10982
21.7%
tijuca 3953
 
7.8%
barra 3653
 
7.2%
da 3639
 
7.2%
ipanema 3455
 
6.8%
jacarepaguá 1813
 
3.6%
recreio 1804
 
3.6%
dos 1804
 
3.6%
bandeirantes 1804
 
3.6%
leblon 1742
 
3.4%
Other values (186) 16035
31.6%
2024-04-25T18:07:39.183818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 91184
24.6%
o 27519
 
7.4%
n 25976
 
7.0%
e 24028
 
6.5%
r 20318
 
5.5%
c 19773
 
5.3%
p 16524
 
4.5%
14676
 
4.0%
C 14193
 
3.8%
b 13340
 
3.6%
Other values (50) 102694
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 370225
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 91184
24.6%
o 27519
 
7.4%
n 25976
 
7.0%
e 24028
 
6.5%
r 20318
 
5.5%
c 19773
 
5.3%
p 16524
 
4.5%
14676
 
4.0%
C 14193
 
3.8%
b 13340
 
3.6%
Other values (50) 102694
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 370225
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 91184
24.6%
o 27519
 
7.4%
n 25976
 
7.0%
e 24028
 
6.5%
r 20318
 
5.5%
c 19773
 
5.3%
p 16524
 
4.5%
14676
 
4.0%
C 14193
 
3.8%
b 13340
 
3.6%
Other values (50) 102694
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 370225
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 91184
24.6%
o 27519
 
7.4%
n 25976
 
7.0%
e 24028
 
6.5%
r 20318
 
5.5%
c 19773
 
5.3%
p 16524
 
4.5%
14676
 
4.0%
C 14193
 
3.8%
b 13340
 
3.6%
Other values (50) 102694
27.7%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct20707
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.96758
Minimum-23.073276
Maximum-22.74969
Zeros0
Zeros (%)0.0%
Negative36008
Negative (%)100.0%
Memory size281.4 KiB
2024-04-25T18:07:40.085919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-23.073276
5-th percentile-23.014033
Q1-22.984712
median-22.97286
Q3-22.956277
95-th percentile-22.910943
Maximum-22.74969
Range0.32358596
Interquartile range (IQR)0.0284346

Descriptive statistics

Standard deviation0.034686656
Coefficient of variation (CV)-0.0015102443
Kurtosis4.1290956
Mean-22.96758
Median Absolute Deviation (MAD)0.01227
Skewness1.2785007
Sum-827016.63
Variance0.0012031641
MonotonicityNot monotonic
2024-04-25T18:07:41.091266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.9980396 53
 
0.1%
-22.9808971 35
 
0.1%
-22.9804488 25
 
0.1%
-22.98456 20
 
0.1%
-22.98353 16
 
< 0.1%
-22.98463 16
 
< 0.1%
-22.98347 16
 
< 0.1%
-22.9792207 16
 
< 0.1%
-22.98431 16
 
< 0.1%
-22.98346 16
 
< 0.1%
Other values (20697) 35779
99.4%
ValueCountFrequency (%)
-23.07327596 1
< 0.1%
-23.07305 1
< 0.1%
-23.07284 1
< 0.1%
-23.07273666 1
< 0.1%
-23.07262 1
< 0.1%
-23.07241 1
< 0.1%
-23.0722 2
< 0.1%
-23.07215409 1
< 0.1%
-23.07215 1
< 0.1%
-23.07211 1
< 0.1%
ValueCountFrequency (%)
-22.74969 1
< 0.1%
-22.74995 1
< 0.1%
-22.75051 1
< 0.1%
-22.75061 1
< 0.1%
-22.75069162 1
< 0.1%
-22.75077 1
< 0.1%
-22.75094 1
< 0.1%
-22.75144 1
< 0.1%
-22.75195 1
< 0.1%
-22.75239 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct22606
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.249377
Minimum-43.723009
Maximum-43.1044
Zeros0
Zeros (%)0.0%
Negative36008
Negative (%)100.0%
Memory size281.4 KiB
2024-04-25T18:07:41.955849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-43.723009
5-th percentile-43.471277
Q1-43.306566
median-43.194825
Q3-43.185573
95-th percentile-43.174845
Maximum-43.1044
Range0.61860934
Interquartile range (IQR)0.12099287

Descriptive statistics

Standard deviation0.099240936
Coefficient of variation (CV)-0.0022946212
Kurtosis1.2008505
Mean-43.249377
Median Absolute Deviation (MAD)0.017098252
Skewness-1.4714596
Sum-1557323.5
Variance0.0098487634
MonotonicityNot monotonic
2024-04-25T18:07:42.593870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.2568208 53
 
0.1%
-43.4224405 35
 
0.1%
-43.4226697 25
 
0.1%
-43.19053 19
 
0.1%
-43.1911 18
 
< 0.1%
-43.19043 17
 
< 0.1%
-43.19024 17
 
< 0.1%
-43.1907952 16
 
< 0.1%
-43.4731697 16
 
< 0.1%
-43.19002 15
 
< 0.1%
Other values (22596) 35777
99.4%
ValueCountFrequency (%)
-43.72300934 1
< 0.1%
-43.71038 1
< 0.1%
-43.70128586 1
< 0.1%
-43.7012179 1
< 0.1%
-43.70074 1
< 0.1%
-43.6998188 1
< 0.1%
-43.6991893 1
< 0.1%
-43.69332 1
< 0.1%
-43.69155 1
< 0.1%
-43.69005613 1
< 0.1%
ValueCountFrequency (%)
-43.1044 1
< 0.1%
-43.10460541 1
< 0.1%
-43.10505 1
< 0.1%
-43.10513196 1
< 0.1%
-43.10552 1
< 0.1%
-43.10575 1
< 0.1%
-43.105767 1
< 0.1%
-43.10579556 1
< 0.1%
-43.10593 1
< 0.1%
-43.10605 1
< 0.1%

room_type
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size281.4 KiB
Entire home/apt
28468 
Private room
6910 
Shared room
 
592
Hotel room
 
38

Length

Max length15
Median length15
Mean length14.353255
Min length10

Characters and Unicode

Total characters516832
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowPrivate room
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 28468
79.1%
Private room 6910
 
19.2%
Shared room 592
 
1.6%
Hotel room 38
 
0.1%

Length

2024-04-25T18:07:43.189360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-25T18:07:43.723760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
entire 28468
39.5%
home/apt 28468
39.5%
room 7540
 
10.5%
private 6910
 
9.6%
shared 592
 
0.8%
hotel 38
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 64476
12.5%
t 63884
12.4%
o 43586
8.4%
r 43510
8.4%
m 36008
 
7.0%
36008
 
7.0%
a 35970
 
7.0%
i 35378
 
6.8%
h 29060
 
5.6%
p 28468
 
5.5%
Other values (9) 100484
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 516832
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 64476
12.5%
t 63884
12.4%
o 43586
8.4%
r 43510
8.4%
m 36008
 
7.0%
36008
 
7.0%
a 35970
 
7.0%
i 35378
 
6.8%
h 29060
 
5.6%
p 28468
 
5.5%
Other values (9) 100484
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 516832
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 64476
12.5%
t 63884
12.4%
o 43586
8.4%
r 43510
8.4%
m 36008
 
7.0%
36008
 
7.0%
a 35970
 
7.0%
i 35378
 
6.8%
h 29060
 
5.6%
p 28468
 
5.5%
Other values (9) 100484
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 516832
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 64476
12.5%
t 63884
12.4%
o 43586
8.4%
r 43510
8.4%
m 36008
 
7.0%
36008
 
7.0%
a 35970
 
7.0%
i 35378
 
6.8%
h 29060
 
5.6%
p 28468
 
5.5%
Other values (9) 100484
19.4%

price
Real number (ℝ)

MISSING  SKEWED 

Distinct3230
Distinct (%)9.4%
Missing1771
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean1211.7196
Minimum0
Maximum552637
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:44.426026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile150
Q1361
median660
Q31114
95-th percentile3135.4
Maximum552637
Range552637
Interquartile range (IQR)753

Descriptive statistics

Standard deviation5790.9374
Coefficient of variation (CV)4.7791069
Kurtosis5727.864
Mean1211.7196
Median Absolute Deviation (MAD)340
Skewness66.967076
Sum41485643
Variance33534956
MonotonicityNot monotonic
2024-04-25T18:07:45.350271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 538
 
1.5%
500 498
 
1.4%
300 398
 
1.1%
800 389
 
1.1%
600 382
 
1.1%
400 374
 
1.0%
350 328
 
0.9%
1500 321
 
0.9%
200 311
 
0.9%
250 304
 
0.8%
Other values (3220) 30394
84.4%
(Missing) 1771
 
4.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
26 3
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
35 1
 
< 0.1%
39 1
 
< 0.1%
40 2
< 0.1%
41 1
 
< 0.1%
42 2
< 0.1%
43 2
< 0.1%
ValueCountFrequency (%)
552637 1
 
< 0.1%
500000 2
 
< 0.1%
214786 1
 
< 0.1%
189982 1
 
< 0.1%
110130 1
 
< 0.1%
100118 1
 
< 0.1%
100000 5
< 0.1%
99000 1
 
< 0.1%
80080 1
 
< 0.1%
69157 1
 
< 0.1%

minimum_nights
Real number (ℝ)

SKEWED 

Distinct71
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4193513
Minimum1
Maximum1125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:46.097564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum1125
Range1124
Interquartile range (IQR)2

Descriptive statistics

Standard deviation22.738877
Coefficient of variation (CV)5.1452974
Kurtosis1033.295
Mean4.4193513
Median Absolute Deviation (MAD)1
Skewness28.220927
Sum159132
Variance517.05651
MonotonicityNot monotonic
2024-04-25T18:07:46.731999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 10443
29.0%
1 9243
25.7%
3 7858
21.8%
4 2883
 
8.0%
5 2446
 
6.8%
7 1011
 
2.8%
10 422
 
1.2%
6 374
 
1.0%
30 301
 
0.8%
15 286
 
0.8%
Other values (61) 741
 
2.1%
ValueCountFrequency (%)
1 9243
25.7%
2 10443
29.0%
3 7858
21.8%
4 2883
 
8.0%
5 2446
 
6.8%
6 374
 
1.0%
7 1011
 
2.8%
8 57
 
0.2%
9 14
 
< 0.1%
10 422
 
1.2%
ValueCountFrequency (%)
1125 1
 
< 0.1%
1000 3
 
< 0.1%
999 3
 
< 0.1%
960 1
 
< 0.1%
730 1
 
< 0.1%
720 1
 
< 0.1%
630 1
 
< 0.1%
500 3
 
< 0.1%
365 38
0.1%
362 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct367
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.545545
Minimum0
Maximum638
Zeros10302
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:48.304612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q320
95-th percentile93
Maximum638
Range638
Interquartile range (IQR)20

Descriptive statistics

Standard deviation40.60038
Coefficient of variation (CV)2.0772191
Kurtosis29.974078
Mean19.545545
Median Absolute Deviation (MAD)4
Skewness4.4850733
Sum703796
Variance1648.3908
MonotonicityNot monotonic
2024-04-25T18:07:49.682427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10302
28.6%
1 3233
 
9.0%
2 2282
 
6.3%
3 1641
 
4.6%
4 1308
 
3.6%
5 1103
 
3.1%
6 870
 
2.4%
7 803
 
2.2%
8 658
 
1.8%
9 641
 
1.8%
Other values (357) 13167
36.6%
ValueCountFrequency (%)
0 10302
28.6%
1 3233
 
9.0%
2 2282
 
6.3%
3 1641
 
4.6%
4 1308
 
3.6%
5 1103
 
3.1%
6 870
 
2.4%
7 803
 
2.2%
8 658
 
1.8%
9 641
 
1.8%
ValueCountFrequency (%)
638 1
< 0.1%
627 1
< 0.1%
587 1
< 0.1%
555 1
< 0.1%
552 1
< 0.1%
537 1
< 0.1%
532 1
< 0.1%
504 1
< 0.1%
500 1
< 0.1%
484 1
< 0.1%

last_review
Date

MISSING 

Distinct1424
Distinct (%)5.5%
Missing10302
Missing (%)28.6%
Memory size281.4 KiB
Minimum2012-02-21 00:00:00
Maximum2023-12-29 00:00:00
2024-04-25T18:07:50.632722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:51.528743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct678
Distinct (%)2.6%
Missing10302
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Mean1.0462771
Minimum0.01
Maximum13.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:52.266989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.04
Q10.2
median0.67
Q31.5
95-th percentile3.33
Maximum13.75
Range13.74
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.1578162
Coefficient of variation (CV)1.1066056
Kurtosis8.2957752
Mean1.0462771
Median Absolute Deviation (MAD)0.53
Skewness2.215832
Sum26895.6
Variance1.3405383
MonotonicityNot monotonic
2024-04-25T18:07:53.151427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 623
 
1.7%
0.06 563
 
1.6%
0.08 494
 
1.4%
0.19 481
 
1.3%
0.04 453
 
1.3%
0.02 440
 
1.2%
0.13 396
 
1.1%
0.25 370
 
1.0%
1 354
 
1.0%
0.17 331
 
0.9%
Other values (668) 21201
58.9%
(Missing) 10302
28.6%
ValueCountFrequency (%)
0.01 234
 
0.6%
0.02 440
1.2%
0.03 296
0.8%
0.04 453
1.3%
0.05 205
 
0.6%
0.06 563
1.6%
0.07 253
0.7%
0.08 494
1.4%
0.09 254
0.7%
0.1 623
1.7%
ValueCountFrequency (%)
13.75 2
< 0.1%
13.52 1
< 0.1%
11.6 1
< 0.1%
11.41 1
< 0.1%
11.32 1
< 0.1%
11.25 1
< 0.1%
11.03 1
< 0.1%
10.54 1
< 0.1%
10.32 1
< 0.1%
10 1
< 0.1%
Distinct58
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4932237
Minimum1
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:53.930351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile43
Maximum185
Range184
Interquartile range (IQR)4

Descriptive statistics

Standard deviation25.677082
Coefficient of variation (CV)2.70478
Kurtosis23.911237
Mean9.4932237
Median Absolute Deviation (MAD)1
Skewness4.7436407
Sum341832
Variance659.31253
MonotonicityNot monotonic
2024-04-25T18:07:54.818897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17249
47.9%
2 5372
 
14.9%
3 2535
 
7.0%
4 1624
 
4.5%
5 1000
 
2.8%
6 924
 
2.6%
7 581
 
1.6%
8 504
 
1.4%
9 432
 
1.2%
12 372
 
1.0%
Other values (48) 5415
 
15.0%
ValueCountFrequency (%)
1 17249
47.9%
2 5372
 
14.9%
3 2535
 
7.0%
4 1624
 
4.5%
5 1000
 
2.8%
6 924
 
2.6%
7 581
 
1.6%
8 504
 
1.4%
9 432
 
1.2%
10 330
 
0.9%
ValueCountFrequency (%)
185 185
0.5%
157 157
0.4%
145 145
0.4%
142 284
0.8%
128 128
0.4%
85 85
 
0.2%
80 80
 
0.2%
77 77
 
0.2%
69 69
 
0.2%
57 57
 
0.2%

availability_365
Real number (ℝ)

ZEROS 

Distinct366
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.41891
Minimum0
Maximum365
Zeros5475
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:55.460225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143
median160
Q3316
95-th percentile364
Maximum365
Range365
Interquartile range (IQR)273

Descriptive statistics

Standard deviation135.55391
Coefficient of variation (CV)0.78618933
Kurtosis-1.535679
Mean172.41891
Median Absolute Deviation (MAD)132
Skewness0.12206185
Sum6208460
Variance18374.861
MonotonicityNot monotonic
2024-04-25T18:07:56.302194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5475
 
15.2%
365 1553
 
4.3%
364 643
 
1.8%
269 498
 
1.4%
358 430
 
1.2%
1 404
 
1.1%
363 396
 
1.1%
362 341
 
0.9%
89 341
 
0.9%
360 330
 
0.9%
Other values (356) 25597
71.1%
ValueCountFrequency (%)
0 5475
15.2%
1 404
 
1.1%
2 90
 
0.2%
3 95
 
0.3%
4 85
 
0.2%
5 74
 
0.2%
6 92
 
0.3%
7 80
 
0.2%
8 72
 
0.2%
9 73
 
0.2%
ValueCountFrequency (%)
365 1553
4.3%
364 643
1.8%
363 396
 
1.1%
362 341
 
0.9%
361 238
 
0.7%
360 330
 
0.9%
359 277
 
0.8%
358 430
 
1.2%
357 227
 
0.6%
356 231
 
0.6%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct99
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1173073
Minimum0
Maximum124
Zeros14214
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size281.4 KiB
2024-04-25T18:07:57.234901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q39
95-th percentile32
Maximum124
Range124
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.662121
Coefficient of variation (CV)1.638558
Kurtosis8.0016977
Mean7.1173073
Median Absolute Deviation (MAD)2
Skewness2.4872391
Sum256280
Variance136.00507
MonotonicityNot monotonic
2024-04-25T18:07:58.155210image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14214
39.5%
1 3431
 
9.5%
2 2400
 
6.7%
3 1721
 
4.8%
4 1282
 
3.6%
5 1074
 
3.0%
6 867
 
2.4%
7 765
 
2.1%
8 693
 
1.9%
9 654
 
1.8%
Other values (89) 8907
24.7%
ValueCountFrequency (%)
0 14214
39.5%
1 3431
 
9.5%
2 2400
 
6.7%
3 1721
 
4.8%
4 1282
 
3.6%
5 1074
 
3.0%
6 867
 
2.4%
7 765
 
2.1%
8 693
 
1.9%
9 654
 
1.8%
ValueCountFrequency (%)
124 1
< 0.1%
120 1
< 0.1%
115 1
< 0.1%
113 1
< 0.1%
108 2
< 0.1%
105 2
< 0.1%
98 2
< 0.1%
96 2
< 0.1%
94 1
< 0.1%
93 2
< 0.1%

license
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing36008
Missing (%)100.0%
Memory size281.4 KiB

Interactions

2024-04-25T18:07:11.369591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:33.837374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:42.367387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:49.319714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:07.612461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:14.080993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:19.615538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:25.516010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:34.952457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:46.988449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:04.252971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:11.928649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:34.512257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:43.053375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:49.952121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:08.868359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:14.520250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:20.000362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:25.904160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:35.951711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:49.284193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:05.073411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:12.494720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:35.095591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:43.723401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:50.592809image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:09.718452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:15.050359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:20.468930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:26.437773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:37.126901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:51.760064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:05.835775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:13.108370image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:35.775589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:44.292146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:52.449715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:10.300707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:15.853597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:21.162394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:27.037884image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:38.127925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:53.364322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:06.520947image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:13.757998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:36.588909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:44.897229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:55.878673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:10.984526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:16.395196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:21.571566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:27.635134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:39.311492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:55.056896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:07.158207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:14.373818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:38.299702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:45.473791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:57.260125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:11.343256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:16.952140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:21.976490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:28.311626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:40.443352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:56.193434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:07.768470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:15.029819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:39.016808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:46.031607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:58.989923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:11.782805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:17.358617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:22.572754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:28.829819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:41.480810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:57.264293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:08.298959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:15.508818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:39.628841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:46.627992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:00.257020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:12.211094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:17.865721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:23.187412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:29.585237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:42.548454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:58.733807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:08.961955image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:16.248887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:40.264020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:47.260205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:02.692215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:12.700506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:18.298924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:23.824543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:31.460877image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:43.614555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:59.900286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:09.610985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:17.149040image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:40.998524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:47.984174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:04.446544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:13.193124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:18.734831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:24.455487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:32.522035image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:44.697732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:01.063458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:10.208558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:17.900896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:41.724763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:05:48.675971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:05.930551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:13.664650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:19.193436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:25.063295image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:33.728679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:06:45.833310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:03.185211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-25T18:07:10.782055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-04-25T18:07:58.763558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
availability_365calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsnumber_of_reviewsnumber_of_reviews_ltmpricereviews_per_monthroom_type
availability_3651.0000.0260.0080.0030.018-0.126-0.093-0.202-0.1820.031-0.1870.076
calculated_host_listings_count0.0261.000-0.1430.105-0.0850.038-0.0770.0540.0780.1070.0610.082
host_id0.008-0.1431.0000.4520.017-0.076-0.181-0.164-0.036-0.1170.1570.053
id0.0030.1050.4521.000-0.0070.017-0.224-0.384-0.111-0.0290.3850.028
latitude0.018-0.0850.017-0.0071.0000.543-0.058-0.026-0.025-0.341-0.0370.110
longitude-0.1260.038-0.0760.0170.5431.0000.0910.1360.141-0.0150.0850.088
minimum_nights-0.093-0.077-0.181-0.224-0.0580.0911.000-0.040-0.0970.156-0.1920.000
number_of_reviews-0.2020.054-0.164-0.384-0.0260.136-0.0401.0000.868-0.0700.5940.041
number_of_reviews_ltm-0.1820.078-0.036-0.111-0.0250.141-0.0970.8681.000-0.0460.8040.067
price0.0310.107-0.117-0.029-0.341-0.0150.156-0.070-0.0461.0000.0130.013
reviews_per_month-0.1870.0610.1570.385-0.0370.085-0.1920.5940.8040.0131.0000.063
room_type0.0760.0820.0530.0280.1100.0880.0000.0410.0670.0130.0631.000

Missing values

2024-04-25T18:07:18.989449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-25T18:07:21.304585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
017878Condo in Rio de Janeiro · ★4.70 · 2 bedrooms · 2 beds · 1 bath68997MatthiasNaNCopacabana-22.96599-43.17940Entire home/apt1357.053112023-12-221.90126929NaN
1216461Rental unit in Rio de Janeiro · 1 bedroom · 1 bed · 1 bath1154263Zeilma , DaNaNFlamengo-22.93990-43.17676Private room734.010NaNNaN13650NaN
2326205Condo in Rio de Janeiro · ★4.57 · 1 bedroom · 1 bed · 1 bath1603206BobNaNCopacabana-22.96825-43.18237Entire home/apt366.031522023-11-211.07529314NaN
325026Rental unit in Rio de Janeiro · ★4.72 · 1 bedroom · 1 bed · 1 bath102840VivianeNaNCopacabana-22.97735-43.19105Entire home/apt865.022752023-12-031.67122829NaN
4326575Rental unit in Rio de Janeiro · ★4.81 · 2 bedrooms · 3 beds · 2 baths1668565Maria JoséNaNCopacabana-22.97696-43.18933Entire home/apt368.042272023-11-211.58124512NaN
5216700Rental unit in Rio de Janeiro · ★4.96 · 1 bedroom · 1 bed · 1 shared bath1118486MoaraNaNLaranjeiras-22.94373-43.19147Private room300.03242023-11-050.1723563NaN
6219250Loft in Rio de Janeiro · ★4.82 · 1 bedroom · 2 beds · 1 bath1134264RicardoNaNSanta Teresa-22.91666-43.17947Entire home/apt254.024312023-12-113.07231225NaN
735764Loft in Rio de Janeiro · ★4.90 · 1 bedroom · 1 bed · 1.5 baths153691Patricia Miranda & PauloNaNCopacabana-22.98107-43.19136Entire home/apt373.034542023-12-172.8216236NaN
8220377Rental unit in Rio de Janeiro · ★5.0 · 1 bedroom · 1 bed · 1 private bath1142424TacianaNaNTijuca-22.92880-43.24046Private room220.0142017-04-060.0342940NaN
9327375Rental unit in Rio de Janeiro · ★4.0 · 6 bedrooms · 16 beds · 5 baths1673501JosyNaNSanta Teresa-22.92129-43.18193Entire home/apt5874.03122020-02-250.0913650NaN
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
359981053711355674934096Rental unit in Rio de Janeiro · ★New · 1 bedroom · 1 bath535697473VictorNaNCopacabana-22.967333-43.181577Entire home/apt487.010NaNNaN22660NaN
359991053716797020259779Rental unit in Rio de Janeiro · ★New · 1 bedroom · 2 beds · 1 bath154114238BrenoNaNBarra da Tijuca-23.010436-43.357412Entire home/apt889.0222023-12-272.04552NaN
360001053720091301615651Rental unit in Rio de Janeiro · ★New · 1 bedroom · 1 bath55209037DiogoNaNCamorim-22.980449-43.422670Entire home/apt229.010NaNNaN12670NaN
360011053736263866696090Rental unit in Rio de Janeiro · ★New · 1 bedroom · 2 beds · 1 bath13411812FernandoNaNCopacabana-22.969913-43.188015Entire home/apt760.020NaNNaN16870NaN
360021053743193407023650Rental unit in Rio de Janeiro · ★New · 1 bedroom · 2 beds · 1 bath113031534JoãoNaNBarra da Tijuca-23.003550-43.340740Entire home/apt600.010NaNNaN22640NaN
360031053746406056980189Home in Rio de Janeiro · ★New · 1 bedroom · 4 beds · 1 bath470173166MarcoNaNBotafogo-22.956393-43.182119Private room200.020NaNNaN8900NaN
360041053756202332288557Rental unit in Rio de Janeiro · ★New · 1 bedroom · 2 beds · 1 bath6000862Omar Do RioNaNCopacabana-22.980547-43.195863Entire home/apt727.020NaNNaN1853580NaN
360051053789340172837654Rental unit in Rio de Janeiro · ★New · 1 bedroom · 1 bed · 1 bath206898000PabloNaNCopacabana-22.968340-43.185855Private room743.010NaNNaN12700NaN
360061053808194231554793Rental unit in Rio de Janeiro · ★New · 1 bedroom · 1 bath536983374IzaiasNaNVidigal-22.996260-43.240830Entire home/apt560.010NaNNaN12490NaN
360071053823261878675052Rental unit in Rio de Janeiro · ★New · 1 bedroom · 1 bed · 1 bath694816SamuelNaNGlória-22.912241-43.171288Private room175.020NaNNaN1190NaN